Learn R Programming

⚠️There's a newer version (8.1-1) of this package.Take me there.

rms (version 4.2-1)

Regression Modeling Strategies

Description

Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. rms is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. rms works with almost any regression model, but it was especially written to work with binary or ordinal regression models, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression.

Copy Link

Version

Install

install.packages('rms')

Monthly Downloads

38,544

Version

4.2-1

License

GPL (>= 2)

Maintainer

Frank E Harrell Jr

Last Published

September 18th, 2014

Functions in rms (4.2-1)

anova.rms

Analysis of Variance (Wald and F Statistics)
calibrate

Resampling Model Calibration
Function

Compose an S Function to Compute X beta from a Fit
Rq

rms Package Interface to quantreg Package
cr.setup

Continuation Ratio Ordinal Logistic Setup
contrast.rms

General Contrasts of Regression Coefficients
sensuc

Sensitivity to Unmeasured Covariables
rms

rms Methods and Generic Functions
ExProb

Function Generator For Exceedance Probabilities
summary.rms

Summary of Effects in Model
psm

Parametric Survival Model
gIndex

Calculate Total and Partial g-indexes for an rms Fit
rms-internal

Internal rms functions
latex.cph

LaTeX Representation of a Fitted Cox Model
validate

Resampling Validation of a Fitted Model's Indexes of Fit
survplot

Plot Survival Curves and Hazard Functions
cph

Cox Proportional Hazards Model and Extensions
rmsOverview

Overview of rms Package
predab.resample

Predictive Ability using Resampling
pphsm

Parametric Proportional Hazards form of AFT Models
matinv

Total and Partial Matrix Inversion using Gauss-Jordan Sweep Operator
validate.Rq

Validation of a Quantile Regression Model
Glm

rms Version of glm
which.influence

Which Observations are Influential
lrm.fit

Logistic Model Fitter
val.surv

Validate Predicted Probabilities Against Observed Survival Times
residuals.cph

Residuals for a cph Fit
bootcov

Bootstrap Covariance and Distribution for Regression Coefficients
pentrace

Trace AIC and BIC vs. Penalty
validate.lrm

Resampling Validation of a Logistic or Ordinal Regression Model
npsurv

Nonparametric Survival Estimates for Censored Data
lrm

Logistic Regression Model
groupkm

Kaplan-Meier Estimates vs. a Continuous Variable
survfit.cph

Cox Predicted Survival
gendata

Generate Data Frame with Predictor Combinations
bj

Buckley-James Multiple Regression Model
datadist

Distribution Summaries for Predictor Variables
print.ols

Print ols
validate.cph

Validation of a Fitted Cox or Parametric Survival Model's Indexes of Fit
survest.psm

Parametric Survival Estimates
rms.trans

rms Special Transformation Functions
orm

Ordinal Regression Model
validate.ols

Validation of an Ordinary Linear Model
plot.xmean.ordinaly

Plot Mean X vs. Ordinal Y
hazard.ratio.plot

Hazard Ratio Plot
bootBCa

BCa Bootstrap on Existing Bootstrap Replicates
plot.Predict

Plot Effects of Variables Estimated by a Regression Model Fit
latexrms

LaTeX Representation of a Fitted Model
Predict

Compute Predicted Values and Confidence Limits
orm.fit

Ordinal Regression Model Fitter
nomogram

Draw a Nomogram Representing a Regression Fit
print.cph

Print cph Results
survest.cph

Cox Survival Estimates
robcov

Robust Covariance Matrix Estimates
predict.lrm

Predicted Values for Binary and Ordinal Logistic Models
ols

Linear Model Estimation Using Ordinary Least Squares
bplot

3-D Plots Showing Effects of Two Continuous Predictors in a Regression Model Fit
residuals.ols

Residuals for ols
val.prob

Validate Predicted Probabilities
Gls

Fit Linear Model Using Generalized Least Squares
rmsMisc

Miscellaneous Design Attributes and Utility Functions
validate.rpart

Dxy and Mean Squared Error by Cross-validating a Tree Sequence
residuals.lrm

Residuals from an lrm or orm Fit
ie.setup

Intervening Event Setup
fastbw

Fast Backward Variable Selection
specs.rms

rms Specifications for Models
vif

Variance Inflation Factors
setPb

Progress Bar for Simulations
predictrms

Predicted Values from Model Fit